Cannot plot time series: problem with datetime parsing

Question

I have this DataFrame (small subset of my real DataFrame):

household   feed            utc_timestamp               kWh
industrial3 area_offices    2015-10-13 11:00:00+00:00   1.940186
industrial3 area_offices    2015-10-13 12:00:00+00:00   4.847656
industrial3 area_offices    2015-10-13 13:00:00+00:00   7.089600
industrial3 area_offices    2015-10-13 14:00:00+00:00   9.682129
industrial3 area_offices    2015-10-13 15:00:00+00:00   12.142578

I want just to plot kWh per date time.

plt.figure(figsize=(15,5))
plt.plot(df["utc_timestamp"].values, df["kWh"].values)
plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1), fancybox=True, ncol=5, labels=['kWh'])
plt.grid(True)
plt.show()

But dates and times are not parsed correctly:

When I run df["utc_timestamp"].values[0], I see that timestamps are of the type numpy.datetime64:

numpy.datetime64('2015-10-13T11:00:00.000000000')

The plot looks are follows:

How can I fix it?

Update:

utc_datetime:

array(['2017-05-22T21:00:00.000000000', '2017-05-22T22:00:00.000000000',
       '2017-05-22T23:00:00.000000000', '2017-05-23T00:00:00.000000000',
       '2017-05-23T01:00:00.000000000', '2017-05-23T02:00:00.000000000',
       '2017-05-23T03:00:00.000000000', '2017-05-23T04:00:00.000000000',
       '2017-05-23T05:00:00.000000000', '2017-05-23T06:00:00.000000000',
       '2017-05-23T07:00:00.000000000', '2017-05-23T08:00:00.000000000',
       '2017-05-23T09:00:00.000000000', '2017-05-23T10:00:00.000000000',
       '2017-05-23T11:00:00.000000000', '2017-05-23T12:00:00.000000000',
       '2017-05-23T13:00:00.000000000', '2017-05-23T14:00:00.000000000',
       '2017-05-23T15:00:00.000000000', '2017-05-23T16:00:00.000000000',
       '2017-05-23T17:00:00.000000000', '2017-05-23T18:00:00.000000000',
       '2017-05-23T19:00:00.000000000', '2017-05-23T20:00:00.000000000',
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       '2015-10-19T00:00:00.000000000', '2015-10-19T01:00:00.000000000',
       '2015-10-19T02:00:00.000000000', '2015-10-19T03:00:00.000000000',
       '2015-10-19T04:00:00.000000000', '2015-10-19T05:00:00.000000000',
       '2015-10-19T06:00:00.000000000', '2015-10-19T07:00:00.000000000',
       '2015-10-19T08:00:00.000000000', '2015-10-19T09:00:00.000000000',
       '2015-10-19T10:00:00.000000000', '2015-10-19T11:00:00.000000000',
       '2015-10-19T12:00:00.000000000', '2015-10-19T13:00:00.000000000',
       '2015-10-19T14:00:00.000000000', '2015-10-19T15:00:00.000000000',
       '2015-10-19T16:00:00.000000000', '2015-10-19T17:00:00.000000000',
       '2015-10-19T18:00:00.000000000', '2015-10-19T19:00:00.000000000',
       '2015-10-19T20:00:00.000000000', '2015-10-19T21:00:00.000000000',
       '2015-10-19T22:00:00.000000000', '2015-10-19T23:00:00.000000000',
       '2015-10-20T00:00:00.000000000', '2015-10-20T01:00:00.000000000',
       '2015-10-20T02:00:00.000000000', '2015-10-20T03:00:00.000000000',
       '2015-10-20T04:00:00.000000000', '2015-10-20T05:00:00.000000000',
       '2015-10-20T06:00:00.000000000', '2015-10-20T07:00:00.000000000',
       '2015-10-20T08:00:00.000000000', '2015-10-20T09:00:00.000000000',
       '2015-10-20T10:00:00.000000000', '2015-10-20T11:00:00.000000000',
       '2015-10-20T12:00:00.000000000', '2015-10-20T13:00:00.000000000',
       '2015-10-20T14:00:00.000000000', '2015-10-20T15:00:00.000000000',
       '2015-10-20T16:00:00.000000000', '2015-10-20T17:00:00.000000000',
       '2015-10-20T18:00:00.000000000', '2015-10-20T19:00:00.000000000',
       '2015-10-20T20:00:00.000000000', '2015-10-20T21:00:00.000000000',
       '2015-10-20T22:00:00.000000000', '2015-10-20T23:00:00.000000000'],
      dtype='datetime64[ns]')

kWh:

array([5.90673828e+03, 5.90689844e+03, 5.90706348e+03, 5.90722852e+03,
       5.90738867e+03, 5.90755371e+03, 5.90771875e+03, 5.90787793e+03,
       5.90804346e+03, 5.90974854e+03, 5.91082324e+03, 5.91210352e+03,
       5.91341357e+03, 5.91433838e+03, 5.91545312e+03, 5.91627344e+03,
       5.91711328e+03, 5.91780859e+03, 5.91799805e+03, 5.91815820e+03,
       5.91831836e+03, 5.91848340e+03, 5.91864844e+03, 5.91880859e+03,
       5.91896875e+03, 5.91912793e+03, 5.91928809e+03, 5.91944824e+03,
       5.91960840e+03, 5.91976855e+03, 5.91992871e+03, 5.92008887e+03,
       5.92024805e+03, 5.92310840e+03, 5.92551855e+03, 5.92694336e+03,
       5.92823828e+03, 5.92883838e+03, 5.93013330e+03, 5.93142334e+03,
       5.93278857e+03, 5.93309863e+03, 5.93325879e+03, 5.93342334e+03,
       5.93358350e+03, 5.93375879e+03, 5.93392871e+03, 5.93409863e+03,
       5.93426855e+03, 5.93444336e+03, 5.93461328e+03, 5.93478809e+03,
       5.93495801e+03, 5.93512793e+03, 5.93530371e+03, 5.93547852e+03,
       5.93564844e+03, 5.93581836e+03, 5.93598828e+03, 5.93615820e+03,
       5.93632812e+03, 5.93650342e+03, 5.93667334e+03, 5.93684375e+03,
       5.93701855e+03, 5.93718848e+03, 5.93736816e+03, 5.93754346e+03,
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       5.94048828e+03, 5.94066797e+03, 5.94083887e+03, 5.94101367e+03,
       5.94119824e+03, 5.94137305e+03, 5.94154883e+03, 5.94172363e+03,
       5.94189844e+03, 5.94207812e+03, 5.94225342e+03, 5.94242871e+03,
       5.94260352e+03, 5.94277832e+03, 5.94294824e+03, 5.94312793e+03,
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       5.94885352e+03, 5.94902832e+03, 5.94919824e+03, 5.94937305e+03,
       5.94954883e+03, 5.94971875e+03, 5.94988867e+03, 5.95005859e+03,
       5.95022852e+03, 5.95040332e+03, 5.95057324e+03, 5.95074316e+03,
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       5.96397852e+03, 5.96520801e+03, 5.96536816e+03, 5.96552832e+03,
       5.96568848e+03, 5.96585840e+03, 5.96601855e+03, 5.96617871e+03,
       5.96633887e+03, 5.96648828e+03, 5.96664844e+03, 5.96679883e+03,
       5.96695801e+03, 5.96710840e+03, 5.96726855e+03, 5.96741797e+03,
       5.96757812e+03, 5.96820850e+03, 5.97068848e+03, 5.97206348e+03,
       5.97341846e+03, 5.97458350e+03, 5.97582373e+03, 5.97721826e+03,
       5.97855322e+03, 5.97928809e+03, 5.97944824e+03, 5.97961328e+03,
       5.97977344e+03, 5.97993359e+03, 5.98009375e+03, 5.98025879e+03,
       5.98041797e+03, 5.98057812e+03, 5.98073828e+03, 5.98089844e+03,
       5.98105859e+03, 5.98121875e+03, 5.98137793e+03, 5.98153809e+03,
       5.98170312e+03, 5.98430322e+03, 5.98551855e+03, 5.98671826e+03,
       5.98790820e+03, 5.98875342e+03, 5.98980859e+03, 5.99043848e+03,
       5.99061816e+03, 5.99080371e+03, 5.99097852e+03, 5.99115820e+03,
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       6.01525342e+03, 6.01540820e+03, 6.01557812e+03, 6.01573828e+03,
       6.01589844e+03, 6.01605859e+03, 6.01621875e+03, 6.01637793e+03,
       6.01653809e+03, 6.01669336e+03, 6.01684863e+03, 6.01700879e+03,
       6.01716797e+03, 6.01732812e+03, 6.01748828e+03, 6.01764844e+03,
       6.01780859e+03, 6.01796875e+03, 6.01812305e+03, 6.01827832e+03,
       6.01843848e+03, 6.01859863e+03, 6.01875342e+03, 6.01890820e+03,
       6.01906836e+03, 6.01922363e+03, 6.01937793e+03, 6.01953809e+03,
       6.01969824e+03, 6.01985840e+03, 6.02001855e+03, 6.02017871e+03,
       6.02033887e+03, 6.02049805e+03, 6.02065820e+03, 6.02081836e+03,
       6.02098340e+03, 6.02114844e+03, 6.02130859e+03, 6.02146875e+03,
       6.02163330e+03, 6.02179346e+03, 6.02195801e+03, 6.02211816e+03,
       6.02227832e+03, 6.02243359e+03, 6.02259375e+03, 6.02274805e+03,
       6.02290820e+03, 6.02306836e+03, 6.02322852e+03, 6.02338330e+03,
       6.02354346e+03, 6.02370312e+03, 6.02386328e+03, 6.02402344e+03,
       6.02418848e+03, 6.02419824e+03, 1.66406250e+00, 5.26416016e+00,
       8.72705078e+00, 1.16298828e+01, 1.45742188e+01, 1.74165039e+01,
       1.94863281e+01, 2.08276367e+01, 2.21367188e+01, 2.34667969e+01,
       2.47944336e+01, 2.61171875e+01, 2.74741211e+01, 2.88139648e+01,
       3.01650391e+01, 3.15078125e+01, 3.28164062e+01, 3.40874023e+01,
       3.53676758e+01, 3.93798828e+01, 4.29316406e+01, 4.66713867e+01,
       5.00278320e+01, 5.19091797e+01, 7.82446289e+01, 7.95625000e+01,
       8.08925781e+01, 8.22324219e+01, 8.35722656e+01, 8.49023438e+01,
       8.62421875e+01, 8.75917969e+01, 8.89316406e+01, 9.02617188e+01,
       9.15844727e+01, 9.28925781e+01, 9.41621094e+01, 9.54243164e+01,
       9.79145508e+01, 1.01777832e+02, 1.05995117e+02, 1.09109375e+02,
       1.10819336e+02, 1.13514648e+02, 1.17570312e+02, 1.20952637e+02,
       1.23735451e+02, 1.26286850e+02, 1.27832520e+02, 1.29133301e+02,
       1.30478027e+02, 1.31793457e+02, 1.33113770e+02, 1.34433105e+02,
       1.35773926e+02, 1.37123535e+02, 1.38443848e+02, 1.39763184e+02,
       1.41072754e+02, 1.42383301e+02, 1.43662598e+02, 1.44922363e+02,
       1.51083496e+02, 1.56077637e+02, 1.62154938e+02, 1.68268066e+02,
       1.74275391e+02, 1.80260742e+02, 1.85813477e+02, 1.89853516e+02,
       1.92335938e+02, 1.93965332e+02, 1.95283203e+02, 1.96605957e+02,
       1.97943359e+02, 1.99266113e+02, 2.00593262e+02, 2.01923340e+02,
       2.03256348e+02, 2.04613281e+02, 2.05936035e+02, 2.07246094e+02,
       2.08562500e+02, 2.09873047e+02, 2.11154785e+02, 2.12445801e+02,
       2.13743652e+02, 2.15025879e+02, 2.16285645e+02, 2.17552734e+02,
       2.18826172e+02, 2.20096191e+02, 2.21385254e+02, 2.22686035e+02,
       2.23972656e+02, 2.25234467e+02, 2.26542969e+02, 2.27843262e+02,
       2.29173340e+02, 2.30482422e+02, 2.31795410e+02, 2.33132812e+02,
       2.34445801e+02, 2.35756348e+02, 2.37083496e+02, 2.38395020e+02,
       2.39703125e+02, 2.41006348e+02, 2.42312500e+02, 2.43603516e+02,
       2.44882812e+02, 2.46163574e+02, 2.47443359e+02, 2.48734863e+02,
       2.50035645e+02, 2.51343262e+02, 2.52652344e+02, 2.53972656e+02,
       2.55292969e+02, 2.56596191e+02, 2.57892578e+02, 2.59176270e+02,
       2.60465332e+02, 2.61795410e+02, 2.63115723e+02, 2.64445801e+02,
       2.65795410e+02, 2.67157227e+02, 2.68533203e+02, 2.69895020e+02,
       2.71256348e+02, 2.72593262e+02, 2.73892578e+02, 2.75152344e+02,
       2.79173523e+02, 2.83101562e+02, 2.86958496e+02, 2.90730469e+02,
       2.94288574e+02, 2.97567871e+02, 3.00740234e+02, 3.04825195e+02,
       3.07103516e+02, 3.08404785e+02, 3.09686035e+02, 3.10984863e+02,
       3.12323730e+02, 3.13654785e+02, 3.14955566e+02, 3.16283203e+02,
       3.17613281e+02, 3.18965332e+02, 3.20326172e+02, 3.21686035e+02,
       3.23033203e+02, 3.24355957e+02, 3.25632812e+02, 3.27965820e+02,
       3.31247559e+02, 3.33462891e+02, 3.36863770e+02, 3.40575195e+02,
       3.43897949e+02, 3.47589844e+02, 3.51118652e+02, 3.54883301e+02,
       3.58193359e+02, 3.60778320e+02, 3.62958008e+02, 3.64407227e+02,
       3.65918945e+02, 3.67416016e+02, 3.68885254e+02, 3.70365723e+02,
       3.71868164e+02, 3.73377930e+02], dtype=float32)

I'd suggest you check out [this question](https://stackoverflow.com/questions/13703720/converting-between-datetime-timestamp-and-datetime64) and then (most likely) just run `df['utc_timestamp'] = pd.to_datetime(df['utc_timestamp'])` — Asmus, Nov 26 '20 at 13:23
@Asmus: Yes, this is what I did already. In my post you see the result after applying `pd.to_datetime`. Otherwise, `utc_timestamp` would be a string. — Fluxy, Nov 26 '20 at 13:41
Oops, my bad. I had missed the `.values` part. Anyway if you question is only about nicely formatting the x-axis then maybe [this answer](https://stackoverflow.com/a/9627970/565489) is relevant? — Asmus, Nov 26 '20 at 13:51

score 0 · Answer 1 · answered Nov 26 '20 at 14:24

An interesting option concerning dates placement on the axis and formatting is to use AutoDateLocator and ConciseDateFormatter.

Of course, utc_timestamp column must be converted to datetime type, e.g. using pd.to_datetime.

Try the below code:

import matplotlib.dates as mdates

plt.figure(figsize=(10,3))
plt.plot(df.utc_timestamp.values, df.kWh)
plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1), fancybox=True, ncol=5, labels=['kWh'])
plt.grid(True)
locator = mdates.AutoDateLocator()
formatter = mdates.ConciseDateFormatter(locator)
ax = plt.gca()
ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(formatter)
plt.show()

For your data sample I got the following picture:

If your DataFrame contains dates / hours from a wider range, the placement of x labels will be suitably adjusted.

ConciseDateFormatter is an elegant tool to "join" date and time labels.

Read the documentation about AutoDateLocator, ConciseDateFormatter and possibly other locators to get acquainted with what you can do with them.

On my full DataFrame, this code creates exactly the same plot as I showed in my post. Of course, I use `pd.to_datetime` and `df.utc_timestamp.values[0]` shows `numpy.datetime64('2015-10-13T11:00:00.000000000')`. So, it should plot well, but for some reason it plots the mess. — Fluxy, Nov 26 '20 at 14:28
Please kindly take a look at my Update. I posted a dataset that shows my issue. Thanks. — Fluxy, Nov 26 '20 at 14:35
My bad, the DataFrame was not sorted by `utc_timestamp` column!!! — Fluxy, Nov 26 '20 at 14:39

Cannot plot time series: problem with datetime parsing

1 Answers1